Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality

Choudhry, Arjun; Sharma, Mandar; Chundury, Pramod; Kapler, Thomas; Gray, Derek W. S.; Ramakrishnan, Naren; Elmqvist, Niklas

doi:10.1109/tvcg.2020.3030358

Cited by 12 publications

(11 citation statements)

References 49 publications

(52 reference statements)

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“…In their system, visualization captions adapt while interacting with the visualizations. Other systems link generated textual explanations with visualizations in different context, for instance, to report analysis findings (e.g., Vis Author Profiles [LB19b]) or to explain causality visualizations (e.g., CauseWorks [CSC ∗ 21]).…”

Section: Related Workmentioning

confidence: 99%

A Deeper Understanding of Visualization‐Text Interplay in Geographic Data‐driven Stories

Latif

Chen

Beck

2021

Computer Graphics Forum

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Data‐driven stories comprise of visualizations and a textual narrative. The two representations coexist and complement each other. Although existing research has explored the design strategies and structure of such stories, it remains an open research question how the two representations play together on a detailed level and how they are linked with each other. In this paper, we aim at understanding the fine‐grained interplay of text and visualizations in geographic data‐driven stories. We focus on geographic content as it often includes complex spatiotemporal data presented as versatile visualizations and rich textual descriptions. We conduct a qualitative empirical study on 22 stories collected from a variety of news media outlets; 10 of the stories report the COVID‐19 pandemic, the others cover diverse topics. We investigate the role of every sentence and visualization within the narrative to reveal how they reference each other and interact. Moreover, we explore the positioning and sequence of various parts of the narrative to find patterns that further consolidate the stories. Drawing from the findings, we discuss study implications with respect to best practices and possibilities to automate the report generation.

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Section: Related Workmentioning

confidence: 99%

A Deeper Understanding of Visualization‐Text Interplay in Geographic Data‐driven Stories

Latif

Chen

Beck

2021

Computer Graphics Forum

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“…Next to application papers implementing human-machine interaction theoretical works related to language-based interaction modeling are explicitly included. Bryan et al, 2017;Kwon et al, 2014;Metoyer et al, 2018;Choudhry et al, 2021;Shi et al, 2021;Chen et al, 2020b) Explanation Generation (Sevastjanova et al, 2018;Hohman et al, 2019;von Landesberger et al, 2021) Produce Annotation (Chen et al, 2010b,a;Vanhulst et al, 2021;Latif et al, 2018;Ren et al, 2017;Latif et al, 2021) Documentation Weaver, 2013, 2015) Visualization Creation (Rashid et al, 2021;Fulda et al, 2016; NLU Method Toolkits and Technologies Semantic Parsing…”

Section: A Scopementioning

confidence: 99%

The Why and The How: A Survey on Natural Language Interaction in Visualization

Voigt¹,

Alaçam²,

Meuschke³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Natural language as a modality of interaction is becoming increasingly popular in the field of visualization. In addition to the popular query interfaces, other language-based interactions such as annotations, recommendations, explanations, or documentation experience growing interest. In this survey, we provide an overview of natural language-based interaction in the research area of visualization. We discuss a renowned taxonomy of visualization tasks and classify 119 related works to illustrate the stateof-the-art of how current natural language interfaces support their performance. We examine applied NLP methods and discuss humanmachine dialogue structures with a focus on initiative, duration, and communicative functions in recent visualization-oriented dialogue interfaces. Based on this overview, we point out interesting areas for the future application of NLP methods in the field of visualization.

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“…In contrast, consider the Analyst's Workspace designed by Hossain et al, 4 which uses entity-based connections to generate storylines, but does not leverage topical information. As another example, consider the causal storytelling visualization technique developed by Choudhry et al, 6 which explicitly models causal relationships, but does not exploit other types of cognitive connections. Thus, we posit that there is a need to develop a narrative representation and extraction model that can leverage all these types of connections.…”

Section: Task-specific Mapsmentioning

confidence: 99%

“…3 Narratives are used in the process of “connecting the dots” between apparently unrelated pieces of information 4,5 and modeling causal relationships. 6…”

Section: Introductionmentioning

confidence: 99%

“…3 Narratives are used in the process of ''connecting the dots'' between apparently unrelated pieces of information 4,5 and modeling causal relationships. 6 Storytelling in general is an accepted metaphor used in visual analytics and analytical reasoning. [7][8][9] However, unlike general visual storytelling, our work focuses specifically on visualizing textual narratives, such as those created by news.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design guidelines for narrative maps in sensemaking tasks

Norambuena

Mitra

North

2022

Information Visualization

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Narrative sensemaking is a fundamental process to understand sequential information. Narrative maps are a visual representation framework that can aid analysts in their narrative sensemaking process. Narrative maps allow analysts to understand the big picture of a narrative, uncover new relationships between events, and model the connection between storylines. We seek to understand how analysts create and use narrative maps in order to obtain design guidelines for an interactive visualization tool for narrative maps that can aid analysts in narrative sensemaking. We perform two experiments with a data set of news articles. The insights extracted from our studies can be used to design narrative maps, extraction algorithms, and visual analytics tools to support the narrative sensemaking process. The contributions of this paper are three-fold: (1) an analysis of how analysts construct narrative maps; (2) a user evaluation of specific narrative map features; and (3) design guidelines for narrative maps. Our findings suggest ways for designing narrative maps and extraction algorithms, as well as providing insights toward useful interactions. We discuss these insights and design guidelines and reflect on the potential challenges involved. As key highlights, we find that narrative maps should avoid redundant connections that can be inferred by using the transitive property of event connections, reducing the overall complexity of the map. Moreover, narrative maps should use multiple types of cognitive connections between events such as topical and causal connections, as this emulates the strategies that analysts use in the narrative sensemaking process.

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Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality

Cited by 12 publications

References 49 publications

A Deeper Understanding of Visualization‐Text Interplay in Geographic Data‐driven Stories

A Deeper Understanding of Visualization‐Text Interplay in Geographic Data‐driven Stories

The Why and The How: A Survey on Natural Language Interaction in Visualization

Design guidelines for narrative maps in sensemaking tasks

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